Method and system for predictive interactive voice recognition

ABSTRACT

A method for a predictive interactive voice recognition system includes receiving a voice call, associating said voice call with a behavioral pattern, and invoking a service context responsive to said behavioral pattern. The system provides advantages of improved voice recognition and more efficient use of the voice user interface to obtain services.

TECHNICAL FIELD

This invention relates to a method and system for predictive interactivevoice recognition.

BACKGROUND OF THE INVENTION

Interactive Voice Recognition (IVR) systems act as a bridge betweencomputer databases and the people that access them. IVR systems arepopular and cost effective, providing a self-service customer interfacefor businesses with little direct labor costs. IVR systems have evolvedover time, providing improvements such as touch-tone replacement wherethe system may, for example, prompt “for information press or say one”.These systems replace touch tone interfaces with speech recognitionapplications that recognize a set of spoken numbers and letters thatappear on a touch tone keypad.

Some improved IVR systems provide directed dialogs where the system may,for example, prompt “would you like ticket pricing or availability?” andthe caller responds with “availability”. Typically, directed dialogsystems are designed to recognize a small set of keywords spoken by acaller. Further improvements to existing IVR systems include naturallanguage processing, where the system may, for example, prompt “whattransaction would you like to perform?” and the caller responds with“transfer 200 dollars from savings to checking”.

SUMMARY OF THE INVENTION

Advantageously, this invention provides a method for predictiveinteractive voice recognition according to claim 1.

Advantageously, according to one example, this invention provides amethod for predictive interactive voice recognition that receives avoice call, associates a voice call with a behavioral pattern, andinvokes a service context responsive to a behavioral pattern.

Advantageously, according to a preferred example, the method recordscaller service requests, creates a plurality of data records responsiveto the caller service requests, categorizes caller service requestsresponsive to a plurality of service contexts, and records a behavioralpattern in response to the categorization. Benefits include takingadvantage of known information to identify a presumed purpose of a callwithout requiring the caller to expressly identify the purpose everytime. The system can then provide more efficient service by allowing thedirect placement into a service context, eliminating requirement of highlevel menus, while allowing for improved accuracy.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates example method steps for implementing this invention;

FIG. 2 illustrates an example system for implementing the method stepsof FIG. 1;

FIG. 3 is an example caller information data structure;

FIG. 4 is an example caller behavioral pattern data structure;

FIG. 5 is an example data structure associating caller behaviors withstored categories; and

FIG. 6 is an example graph of a behavioral pattern.

DESCRIPTION OF AN EXEMPLARY EMBODIMENT

Referring to FIGS. 1 and 2, example method steps for interactive voicerecognition 100 begin at 102. An IVR receives a voice call 104, in which(a) the caller immediately speaks to the IVR without waiting for agreeting, prompt, or menu, or (b) the caller waits for a greeting,prompt, or menu from the IVR.

The IVR records the incoming caller utterance stream 106 and retains theutterances in a buffer for tokenization 108. The utterance stream may berecorded 106 in a memory (FIG. 2, 224) associated with a telematics unit(FIG. 2, 216). Alternatively, caller utterances are tokenized 108 asthey are received. Tokenization comprises separating the caller'sutterances into discrete entities or symbols representing individualwords. The tokenization process comprises receiving an analog acousticsignal (the caller utterances), converting the acoustic signal to aquantized digital signal, and constructing overlapping framesrepresenting the digital signals. Tokenization, including quantizationand frame construction, are well known in the art.

In one embodiment, words uttered by the caller are separated by thecaller, with the caller intentionally pausing between words. This methodis known as isolated word recognition, wherein the IVR requires a periodof quiescence between utterances. In another embodiment the words spokenby the caller may be continuous. This method is known as continuousspeech recognition. Both isolated word recognition and continuous speechrecognition are well known in the art.

The tokens are assigned a confidence factor 110, with the confidencefactor predicting the degree of belief that the tokens are associatedwith or represent particular words. For example, confidence factorsrepresented as probabilities may be assigned via Bayes' Theorem, wherethe probability P of a word is based on in incoming signal or token,such thatP(word|signal)=P(word)P(signal|word)/P(signal).Bayes' Theorem is well known in the art.

The identification of the caller, if known, is used to access previouscaller behavior 112. In one embodiment the caller may be, for example, atelematics service subscriber. For example, the telematics servicesubscriber may repeatedly ask for navigation instruction, being a realestate agent visiting new addresses daily and relying heavily onnavigation routing instructions. In another example, the telematicsservice subscriber may be a traveling salesperson frequently requestingaccess to phone mail and email within a mobile vehicle in addition tonavigation routing instructions.

In another example, the information contained in the caller behavioralpattern may be the aggregate of the behavior of many callers requestingservices or responding to events or notifications issued by a serviceprovider. For example, if the caller is a telematics service subscriber,and the telematics service provider issues a subscription renewalnotification, then the average caller response time to the renewalnotification comprises an aggregate behavior pattern.

The information contained in the caller behavioral pattern is used todetermine a confidence factor to be compared with a confidence factordetermined for the caller utterance. In one embodiment, a confidencefactor may be determined by Bayes' Theorem, where the probability P of acaller preference is based on previous behavior, such thatP(pref|behavior)=P(behavior)P(pref|behavior)/P(pref).In this embodiment, the confidence factor, represented as a probability,is stored in a memory (FIG. 2, 224) associated with a telematics unit(FIG. 2, 216).

In another example, the caller's previous behavior is based on howfrequently the caller has used a particular service or set of services.

In yet another example, the caller utterance may not be present, or ifpresent, yields no definitive confidence factor, and only the confidencefactor for the behavioral preference is utilized to invoke a service,such as, for example, a navigation routing request.

In method step 114, the caller identification information such as, forexample, vehicle identification number (VIN) or personal identificationnumber (PIN), is used to access stored caller information. Thetelematics service provider may offer various subscription packagescontaining different service options, such as, for example, navigationand concierge services in one package, and basic emergency services inanother. Stored caller information comprises caller identificationinformation and the class of services a subscriber is entitled to. Forexample, if a caller is a telematics service subscriber, and the callersubscribes to a basic package that does not provide concierge services,caller identification information may preclude use of the conciergeservices.

Referring now also to FIG. 3, information within the calleridentification database (FIG. 2, 244) is structured such that theidentification of the caller is used to index into a data structurerepresenting the services the caller subscribes to (FIG. 3, 300).

The confidence factors assigned to the utterance tokens 110, the callerbehavior pattern 112, along with the structured stored callerinformation 114 are normalized in step 116. The step of normalizationcomprises transforming the input data to a common format orrepresentation, such as, for example, transforming numerical andtextural data to a common textural format. The common textural formatmay be, for example, represented in ASCII (American Standard Code forInformation Interchange). In another embodiment the data may berepresented symbolically, where the records from the caller behaviorpattern are represented as variables. For example, a data recordvariable may be established for a pattern of navigation request calls.This variable may be assigned the name NAV_CALLS, where the actualnumber of calls may be substituted in other method steps. In yet anotherembodiment, the data may be represented numerically.

Preferably, a non-existent caller utterance resulting in the absence ofcaller utterance data is ignored in any normalization process orcalculation.

Referring now to step 118 the confidence factors determined for thecaller's previous behavior and the uttered token or token stream arecompared. For example, the confidence factor for a navigation routingrequest may be high and the confidence factor for an uttered tokenrepresenting a navigation request may be high. The similarly highconfidence factors indicate that a navigation request was made.

If the caller utterance token is not present, there is no confidencefactor comparison and the confidence factor used for the callerbehavioral pattern is singularly utilized. In another embodiment, aconfidence factor for the aggregate user behavior is utilized. Inanother embodiment, a confidence factor for individual user behavior isutilized.

Also a composite confidence factor may be generated as a result ofutilizing the confidence factor for the uttered token and the confidencefactor for the behavioral pattern as arguments in Bayes' formula. Inanother example, if the utterance confidence factor for the selectedservice is too low, the presumption can be made that the user desiresanother service, in which case the system exits the current servicecontext to a higher level menu, another context or an operator.

Referring to step 120, the compared confidence factors are used toinvoke a service context. A service context is the presumed service thatthe caller desires as indicated by the confidence factors. For example,if the confidence factors exceed a threshold indicating that the call isa navigation request, the caller is placed into a navigation servicevoice interface. This voice interface is tailored according to oneskilled in the art with voice menu options for navigation servicecommands. By narrowing the voice menu selections primarily to navigationcommands when navigation commands are expected, it is possible toimprove the accuracy of voice recognition (both discrete and continuousspeech) due to the statistical advantages by having a reduced menu set.Of course, the navigation context is only one example, and the numberand types of service contexts are limited only by the designer's choiceof desired services.

In the even that the confidence factors do not create a strongindication of a particular service context, then the user is enteredinto a general service context designed to identify the desired servicethrough voice user interface interactions. In each service context, theuser is provided menu options to exit the particular context or move toanother context. This accounts for inadvertent placement into the wrongcontext and users who desire multiple services.

In another example, an advisor 124 may be invoked if a confidence factorfor a particular domain is not adequately determined. The advisor may behuman or an automaton or virtual advisor. Live advisor interactions maybe included in any given service context as desired by the systemdesigner.

Method step 122 records the most recent caller behavior in thebehavioral pattern database. The behavioral pattern database may resideat a call center (FIG. 3, 238) within a subscriber information database(FIG. 3, 244). The behavioral pattern 122 is updated by categorizing thecaller service requests into specific contexts, such as navigation,vehicle service, point of interest inquiry, information service request,etc. and then stored with relevant parameters. The updated behavioralpattern is then available to method step 112. A database is maintainedfor individual caller behavior patterns, aggregate caller behaviorpatterns, or both individual and aggregate caller behavior. The methodsteps end at 128.

In FIG. 2, the system 200 includes a vehicle 210, a vehiclecommunications network 212, a telematics unit 216, one or more wirelesscarrier systems 232, one or more communication networks 234, one or moreland networks 236, and one or more call centers 238. In one embodiment,vehicle 210 is implemented as a mobile vehicle with suitable hardwareand software for transmitting and receiving voice and datacommunications. System 200 may include additional components notrelevant to the present discussion but well known in the telematicsarts. Mobile vehicle communication systems are known in the art.

Vehicle 210, via vehicle communication network 212, sends signals fromthe telematics unit 216 to various units of equipment and systems 214within the vehicle 210 to perform various functions such as unlocking adoor and executing personal comfort settings. In facilitatinginteraction among the various communications and electronic modules,vehicle communications network 212 utilizes interfaces such ascontroller area network (CAN), ISO standard 11989 for high speedapplications, ISO standard 11519 for lower speed applications, andSociety of Automotive Engineers (SAE) standard J1850 for high speed andlower speed applications. Vehicle communications network 212 is alsoreferred to as a vehicle bus.

Vehicle 210, via telematics unit 216, sends and receives radiotransmissions from wireless carrier system 232. Wireless carrier system232 is implemented as a cellular telephone system or any other suitablesystem for transmitting signals between vehicle 210 and communicationsnetwork 234.

Telematics unit 216 includes a processor 218 coupled to a wireless modem220, a global positioning system (GPS) unit 222, an in-vehicle memory224, a microphone 226, one or more speakers 228, and an embedded orin-vehicle mobile phone 230. For example, referring to FIGS. 1 and 2, acaller may initiate a call to an IVR via microphone 226 coupled to thein-vehicle or mobile phone 230 associated with the telematics unit 216.Caller utterances into the microphone 226 are received (FIG. 1, 104) ata call center 238, which tokenizes the utterance stream (FIG. 1, 106)for further processing. In one embodiment, the tokenized utterances areplaced in a subscriber information database 244 at the call center 238.The IVR may be hosted at the call center or at a remote location.

In other example, telematics unit 216 may be implemented without one ormore of the above listed components, such as, for example, speakers 228.It is understood that the speaker 228 may be implemented as part of thevehicle audio system, which accepts audio and other signals fromtelematics unit 216 as is known in the art. Telematics unit 216 mayinclude additional components and functionality as determined by thesystem designer and known in the art for use in telematics units.

Processor 218 may be implemented as a micro controller, controller,microprocessor, host processor, or vehicle communications processor. Inanother embodiment, processor 218 is implemented as an applicationspecific integrated circuit (ASIC). Alternatively, processor 218 isimplemented as a processor working in conjunction with a centralprocessing unit (CPU) performing the function of a general-purposeprocessor.

GPS unit 222 provides latitude and longitude coordinates of the vehicle110 responsive to a GPS broadcast signal received from one or more GPSsatellites (not shown). In-vehicle mobile phone 230 is a cellular typephone, such as, for example an analog, digital, dual-mode, dual-band,multi-mode or multi-band cellular phone.

Associated with processor 218 is a real time clock (RTC) 231 providingaccurate date and time information to the telematics unit hardware andsoftware components that may require date and time information. In oneembodiment date and time information may be requested from the RTC 231by other telematics unit components. In other embodiments the RTC 231may provide date and time information periodically, such as, forexample, every ten milliseconds.

Processor 218 executes various computer programs that interact withoperational modes of electronic and mechanical systems within thevehicle 210. Processor 218 controls communication (e.g. call signals)between telematics unit 216, wireless carrier system 232, and callcenter 238.

Processor 218 generates and accepts digital signals transmitted betweentelematics unit 216 and a vehicle communication network 212 that isconnected to various electronic modules in the vehicle. In one mode,these digital signals activate the programming mode and operation modeswithin the electronic modules, as well as provide for data transferbetween the electronic modules. In another mode, certain signals fromprocessor 218 are translated into voice messages and sent out thoughspeaker 228.

Associated with processor 218 is software 250 for monitoring andrecording the incoming caller utterances.

Communications network 234 includes services from one or more mobiletelephone switching offices and wireless networks. Communication network234 connects wireless carrier system 232 to land network 236.Communications network 234 is implemented as any suitable system orcollection of systems for connecting wireless carrier system 232 tovehicle 210 and land network 236.

Land network 236 connects to communications network 234 to call center238. In one embodiment, land network 236 is a public switched telephonenetwork (PSTN). In another embodiment, land network 236 is implementedas an Internet Protocol (IP) network. In other embodiments, land network236 is implemented as a wired network, an optical network, a fibernetwork, other wireless network, or any combination thereof. Landnetwork 236 is connected to one or more landline telephones.Communication network 234 and land network 236 connect wireless carriersystem 232 to call center 238.

Call center 238 contains one or more voice data switches 240, one ormore communication services managers 242, one or more communicationservices databases 244 containing subscriber profile records, subscriberbehavioral pattern, and subscriber information, one or morecommunication services advisors 248, and one or more network systems248.

Switch 240 of call center 238 connects to land network 236. Switch 240transmits voice or data transmissions from call center 236, and receivesvoice or data transmissions from telematics unit 238 in vehicle 210through wireless carrier system 232, communications network 234, andland network 236. Switch 240 receives data transmissions from or sendsdata transmissions to one or more communication service managers 242 viaone or more network systems 248. Subscriber preferences or settings aretransmitted to the vehicle during a data call and stored within memoryin the vehicle telematics unit 216. The data calls are scheduled inresponse to an update of a subscriber profile record.

Call center 238 contains one or more service advisors 246. In oneembodiment, service advisor 246 may be human. In another embodiment,service advisor 246 may be an automaton.

Referring to data structure 300, when a voice call is received at anIVR, a caller may be identified 302 by, for example, a PIN (PersonalIdentification Number), VIN (Vehicle Identification Number), or otheridentification means. In one embodiment, the caller identification datais transformed and generated into a key that indexes into a databasecontaining stored caller information. The caller identificationinformation may be stored at a call center 238 within a subscriberinformation database 244 or in a subscriber information database hostedat a third party facility (not shown).

In one embodiment, a hashing algorithm is used for key generation. Inanother embodiment, a combination of bit fields comprised of a callerPIN and VIN are used for key generation. Database key generation is wellknown in the art.

The caller identifier key 302 indexes 320 into a service Package 1 304of which the caller subscribes. The service packages contain specificservices such as Service 1 306, which may, for example, be comprised ofbasic safety and security services. Service 2 308, includes navigationrouting services, and Service n 310 comprising concierge services.Alternatively the caller may subscribe to service Package 2 312 thatcontains Service 1 314, Service 2 316 through Service n 318 that maycontain a subset or superset of the services offered in Package 1 304.For example, Package 2 312 Service 1 314 may provide concierge servicesthat allow the subscriber to make hotel or restaurant reservations viatelematics unit (FIG. 2, 216). In this embodiment the generated key willindex 322 into Package 2 312. Package m may contain other services, suchas, for example financial transaction services.

Referring now also to FIG. 4, a call is identified at block 302 (FIG. 3)and the generated key associated with the caller 402 indexes into adatabase containing stored caller behavior. In one embodiment the keyindexes into the first service, Service 1 404 in the caller'ssubscription package. For example, Service 1 404 may be a safety andsecurity service. Associated with Service 1 404 is Behavior 1 406, whichis comprised of the aggregate subscriber behavior for Service 1 404 oran individual subscriber's behavior.

Other services included in the caller's subscription package may includeService 2 408 with Behavior 2 410 through Service n 412 through Behaviorn 414. For example, Service 2 408 may be a subscription renewal servicerequested by callers. The associated Behavior 410 may include, forexample, the average time subscribers call to renew their subscriptionsfrom the time a subscription renewal notification is issued.

Parameters associated with Behaviors 406, 410 through 414 may be timesensitive, such as, for example the average time between the issuance ofa notice and caller response time. In another example, the parametersassociated with Behaviors 406, 410 through 414 may be locationsensitive, such as, for example, navigation routing requestcharacteristics based on callers driving in a common, complex urbanlocation. The individual data structures representing caller behaviorpatterns are utilized as components or records in an aggregate callbehavior pattern data structure.

Referring now also to FIG. 5 and the data structure for predictiveinteractive voice recognition 500, a call is identified at block 302(FIG. 3). The generated key associated with the caller 502 indexes intoa database containing stored caller categories 504, 508, 512 and storedcaller behaviors 506, 510, and 514. Category 1 504 includes datarepresenting and identifying subscribers that have been sent renewalnotifications. The associated behavior, Behavior 1 506, includes datarepresenting the average time a subscriber responds to the said renewalnotice.

Category 2 508 includes a list of subscribers that are within the firstthirty days of a vehicle purchase. The associated behavior, Behavior 2510, includes the average time a subscriber initiates a first orparticular service request within the said thirty-day period. Categoriesthrough Category n 512 and associated Behavior n 514 are limited only bythe capacity of the entity that hosts the data structure and the designchoices of the system designer.

Referring now to FIG. 6, in graph behavioral pattern 600, the Y axis 602represents a probability value with respect to time P(t) and the X axis604 represents time t. Event 606 represents, for example, a subscriptionrenewal notice for a service delivered to an IVR system caller. In thisexample, the subscription renewal notice may be for vehicle telematicsservices such as navigation assistance or emergency assistance.

Reference 608 represents peak responses from recipients of 606.Reference 610 represents the time displacement from the issuance of theevent 606 to the peak responses 608. For example, reference 610represents the time displacement from the issuance of, for example, asubscription renewal notice to the peak response from subscribers.

Reference 612 represents another event, such as, for example a secondissuance of a subscription renewal notice, with 614 representing thepeak caller response to the second subscription renewal notice 612.Reference 616 represents the time displacement from the issuance of thesecond subscription renewal notice 612 to the peak caller response 614.

Reference 618 represents yet another event, such as, for example a thirdissuance of a subscription renewal notice, with 620 representing thepeak caller response to the third subscription renewal notice 618.Reference 622 represents the time displacement from the issuance of thethird subscription renewal notice 618 to the peak caller response 620.

Time intervals for the peak caller responses 608, 614, 620, comprise anaggregate caller behavioral pattern. This aggregate may be determined byfinding the average of the peak caller responses 608, 614, and 620 andcalculating the variance with respect to time of the peak callerresponses 608, 614, and 620 and the issuance of the events 606, 612, and618.

In this example, if a caller initiates a call to an IVR within adetermined time interval, based on the aggregated caller behaviorpattern, the call is assigned a confidence factor representing a beliefthat the call is a subscription renewal call and a subscription renewalservice context is invoked.

1. A method for a predictive interactive voice recognition systemcomprising the steps of: receiving a voice call; associating said voicecall with a behavioral pattern; and invoking a service contextresponsive to said behavioral pattern.
 2. The method of claim 1 alsocomprising the steps of: receiving a caller utterance; determining,responsive to the caller utterance, a confidence factor representativeof a degree of certainty that the caller utterance matches the servicecontext; and exiting the service context if the utterance does not matchthe service criteria.
 3. The method of claim 1 also comprising the stepsof: recording caller service requests; creating a plurality of datarecords responsive to the caller service requests; categorizing thecaller service requests responsive to a plurality of service contexts;and recording a caller behavioral pattern in response to thecategorizing.
 4. The method of claim 1, also comprising the steps of:identifying a plurality of parameters related to the caller and thereceived voice call; and categorizing the parameters into the behavioralpattern.
 5. The method of claim 3, also comprising the step ofretrieving stored caller information, wherein the step of categorizingis also responsive to the stored caller information.
 6. The method ofclaim 3 also comprising the step of assigning a confidence factorresponsive to said caller behavioral pattern.
 7. The method of claim 1wherein the step of invoking comprises: generating a caller utteranceconfidence factor responsive to the received voice call; generating abehavior pattern confidence factor responsive to the received voice calland stored data; and generating a composite confidence factor responsiveto the caller utterance confidence factor and the behavior patternconfidence factor, wherein the step of invoking is responsive to thecomposite confidence factor.
 8. A method of predictive interactive voicerecognition comprising the steps of: receiving a call from a vehiclewith a telematics unit to an interactive voice recognition system;associating said call with a behavioral pattern comprising previouscaller service requests; and invoking a service context responsive tosaid behavioral pattern.
 9. A system for predictive interactive voicerecognition comprising: means for receiving a voice call; means forassociating said voice call with a behavioral pattern; and means forinvoking a service context responsive to said behavioral pattern. 10.The system of claim 9 further comprising: means for the determination ofa confidence factor responsive to a caller utterance, wherein the meansfor invoking the service context is responsive to the confidence factor.11. The system of claim 9 further comprising: means for recording callerservice requests; means for creating a plurality of data recordsresponsive to said caller service request; and means for categorizingcaller service requests responsive to a plurality of service contexts;wherein the behavior pattern is responsive to the means forcategorizing.